Robotic Nurse Assistant Market by Component (Hardware, Services, Software), Mobility (Stationary, Tracked, Wheeled), Application, End User - Global Forecast 2025-2032
Description
The Robotic Nurse Assistant Market was valued at USD 1.26 billion in 2024 and is projected to grow to USD 1.45 billion in 2025, with a CAGR of 15.11%, reaching USD 3.88 billion by 2032.
Introduction to the Robotic Nurse Assistant opportunity, outlining core capabilities, clinical priorities, and strategic imperatives for healthcare leaders
Robotic nurse assistants are emerging as a pivotal technology at the intersection of clinical workflow transformation and healthcare operations optimization. This introduction contextualizes the technology’s core capabilities-autonomous material handling, medication management, patient monitoring, rehabilitation assistance, and telepresence-and explains how they converge to address pressing clinical priorities such as staff workload reduction, infection control, and continuity of care. The intent here is to orient executive readers to the device classes, typical deployment environments, and primary clinician and patient benefits without presupposing prior technical familiarity.
Clinicians and administrators increasingly demand solutions that integrate with electronic health records, adhere to strict safety protocols, and provide demonstrable improvements in task efficiency. Robotic nurse assistants are designed to operate within these constraints, offering repeatable, auditable task execution while freeing clinicians to focus on higher-value cognitive tasks. The remainder of this summary distills the essential strategic considerations for health system leaders, procurement teams, and technology partners contemplating pilot programs or scaled rollouts, emphasizing interoperability, clinical validation, and the organizational change aspects necessary to realize the technology’s value.
Transformative technological, operational, and clinical shifts reshaping nursing workflows and patient care through robotic nurse assistants across care settings
Over the past several years, multiple converging trends have accelerated the readiness and appetite for robotic nurse assistants. Advances in sensor fidelity and perception algorithms have improved safe interaction with patients and dynamic clinical environments, while modular hardware and software architectures have increased the speed of integration with hospital systems. At the same time, clinical pressure points-workforce shortages, rising acuity, and the need for enhanced infection prevention-have shifted executive priorities toward automation that can reliably handle routine tasks.
Operational models are also evolving: procurement teams are favoring reimbursable-capable technologies and service-oriented commercial arrangements that reduce up-front capital risk. From an implementation standpoint, successful pilots have leaned on multidisciplinary governance, measurable clinical outcomes, and iterative training programs. As these trends mature, the landscape is moving from isolated demonstrations to integrated workflows, and health systems that align procurement, clinical champions, and IT strategies will be best positioned to scale robotic nurse assistant deployments effectively.
Assessment of United States tariff changes through 2025 and their operational, procurement, and supply chain impacts on robotic nurse assistants
United States tariff measures enacted or announced through 2025 have introduced new layers of procurement complexity for organizations acquiring robotics components and finished systems. Tariff adjustments on electronics, precision components, and certain robotics assemblies can increase landed costs and elongate procurement timelines, particularly where supply chains rely on cross-border subassembly and just-in-time inventory practices. Procurement teams must therefore reconcile short-term price and timing impacts with longer-term sourcing strategies to preserve both budgetary discipline and implementation schedules.
The practical implications extend beyond cost. Tariff-driven supplier reconfiguration can alter lead times for critical parts such as actuators and vision sensors, prompting engineering teams to qualify alternative components or redesign subsystems to maintain functionality within a different supplier ecosystem. In parallel, legal and compliance teams should reassess contract terms, warranties, and maintenance agreements to reflect any changes in component origin or manufacturer responsibility. To maintain continuity of service and project momentum, decision-makers should incorporate tariff sensitivity into supplier evaluations, prioritize contractual flexibility, and develop mitigation playbooks that include dual-sourcing, inventory buffering for critical spares, and phased deployments aligned with procurement windows.
Segmentation insights on how applications, components, end-user settings, and mobility choices shape clinical value, procurement, and integration
Segmentation-driven insights illuminate how design choices and clinical applications translate into differentiated value propositions for purchasers and clinicians. When considered by application, logistics functions such as supply transport and waste disposal favor robust mobility platforms with secure payload handling and clear route mapping, whereas medication management use cases focused on adherence monitoring and automated dispensing place a premium on secure access controls, audit trails, and tight integration with pharmacy systems. Patient monitoring applications that emphasize fall detection and vital signs monitoring require high-resolution sensors and reliable event escalation pathways, while rehabilitation offerings for cognitive therapy assistance and physical therapy assistance demand adaptable interfaces and evidence-based therapeutic content. Telepresence implementations span remote consultation and remote surgery assistance, each with divergent latency, imaging, and regulatory requirements that directly influence platform selection.
Breaking the technology down by component clarifies procurement and maintenance priorities. Hardware investments concentrate on actuators and sensors; actuator selections between electric and hydraulic embodiments shape power profiles and maintenance cycles, while sensor choices such as touch and vision sensors govern interaction fidelity and environment awareness. Service components-installation, maintenance, and training-define the total cost of ownership and determine how quickly clinical staff achieve safe operational competence. Software layers, including navigation modules and user interface frameworks, further differentiate solutions; navigation software dictates autonomy levels and route optimization, while interface choices between touchscreen and voice recognition materially affect clinician acceptance and patient interaction. End-user segmentation across clinics, home healthcare, hospitals, and long-term care facilities maps directly to requirements for scale, regulatory compliance, and on-site technical support, and mobility classifications of stationary, tracked, and wheeled platforms influence physical integration, facility access, and task suitability. Taken together, these segmentation lenses offer a structured way to align clinical needs, procurement constraints, and deployment pathways to achieve reliable clinical outcomes.
Regional analysis highlighting distinct adoption drivers, regulatory dynamics, reimbursement environments, and partnership models across major global regions
Regional dynamics present materially different adoption drivers and operational constraints that affect how health systems approach robotic nurse assistant procurement and deployment. In the Americas, buyers frequently emphasize integration with diverse electronic health record systems, cost containment, and pilot-to-scale pathways that demonstrate staff time savings and measurable safety improvements. Regulatory clarity and reimbursement discussions often focus on device classification and evidence of clinical benefit, which influences timeline expectations and the structure of commercial agreements.
In Europe, Middle East & Africa, regulatory frameworks and procurement models vary significantly between jurisdictions, creating both challenges and opportunities for localization of software, language support, and service delivery. Reimbursement and public procurement mechanisms in certain European markets can favor solution providers with established clinical partnerships, whereas markets in the Middle East and Africa may emphasize rapid capability deployment and resilience in less predictable operating environments. In Asia-Pacific, accelerated adoption is driven by ageing populations, concentrated urban healthcare systems, and aggressive national digital health initiatives; these factors encourage solutions that can scale across hospital networks and accommodate language and workflow customizations. Each region’s labor dynamics, regulatory posture, and infrastructure maturity should inform vendor selection, pilot design, and expected time-to-value for deployments.
Competitive company intelligence revealing product positioning, go-to-market plays, R&D focus areas, and alliance strategies among leading providers
Company-level dynamics are central to how robotic nurse assistant solutions evolve and how customers should evaluate options. Leading providers differentiate across product modularity, integration toolkits, and service delivery models; those that offer clear upgrade paths, API-based interoperability, and structured service contracts will typically lower barriers for enterprise adoption. Competitive positioning also reflects R&D focus areas such as perception resiliency, human-robot interaction design, and autonomous navigation in cluttered clinical environments.
Strategic alliances and channel partnerships play an outsized role in accelerating deployment, particularly when robotics vendors collaborate with established medical device manufacturers, IT integrators, or global clinical services firms. From a procurement perspective, organizations should assess not only the baseline performance of platforms but also a vendor’s capacity for sustained field support, regulatory compliance track record, and roadmap alignment with hospital digitization priorities. Due diligence that incorporates site visits, proof-of-concept results, and service level commitments will reveal which providers are prepared to move from pilot to enterprise-scale support.
Actionable recommendations to prioritize clinical integration, procurement efficiency, regulatory readiness, and scalable operational deployment
Successful adoption of robotic nurse assistants requires a clear sequence of strategic and operational actions that balance clinical safeguards with scalability. First, establish multidisciplinary governance led by clinical champions, procurement, and IT to set success criteria and measurable outcomes. This governance structure should define safety thresholds, integration priorities, and phased adoption milestones so that pilots generate rigorous evidence that justifies subsequent investment. Next, prioritize interoperability by insisting on open standards, API capabilities, and integration proofs with core systems such as electronic health records and pharmacy dispensing platforms to minimize custom engineering and reduce time to clinical utility.
Operational readiness also requires targeted workforce planning and competency development. Invest in role-based training programs that accelerate confidence among nursing staff and define escalation protocols for exceptions. From a procurement standpoint, favor commercial models that align payment to outcomes where feasible, include defined service level agreements for uptime and response times, and allow for component-level upgrades without full system replacement. Finally, adopt an iterative deployment approach: begin with high-frequency, low-complexity tasks to demonstrate reliability, then progressively expand into higher-complexity clinical interactions as the technology and staff proficiency mature. This pathway minimizes disruption while producing measurable value and stakeholder buy-in.
Research methodology explaining data sources, stakeholder interviews, technology assessments, validation steps, and analytical frameworks used to inform findings
The research approach combined structured primary engagements with technology assessments and secondary analysis to triangulate insights. Primary inputs included interviews with clinicians, procurement leaders, biomedical engineers, and system integrators to capture operational realities and adoption pain points. These stakeholder conversations were supplemented by observational assessments of live pilot deployments to validate user interaction models, fault modes, and service requirements in real-world conditions.
Secondary analysis incorporated product specifications, regulatory filings, and peer-reviewed clinical literature to assess safety profiles and efficacy evidence across application areas. Technology assessments focused on hardware durability, sensor performance, actuator reliability, software architecture, and cybersecurity posture. Throughout the process, findings were validated through cross-referencing with practitioner feedback and iterative review with domain experts to ensure that conclusions reflect both empirical observation and practical applicability for decision-makers.
Strategic conclusion synthesizing implications for clinical leaders, procurement teams, technology partners, and policymakers to guide adoption decisions
In conclusion, robotic nurse assistants represent a practical lever to improve operational efficiency, enhance patient safety, and mitigate workforce constraints when implemented with disciplined governance and clear clinical objectives. The technology is most effective when narrowly scoped to high-frequency tasks that relieve clinicians of repetitive work, integrated tightly with digital health systems, and supported by vendor service models that address maintenance and training.
Decision-makers should treat early deployments as learning systems that produce evidence for broader adoption. By balancing procurement flexibility, regulatory alignment, and workforce readiness, health systems can convert pilot successes into sustainable services that enhance both clinician satisfaction and patient outcomes. The strategic imperative is to adopt a measured, data-driven pathway forward that privileges interoperable design, validated safety, and incremental scale rather than seeking immediate, wholesale replacement of existing workflows.
Please Note: PDF & Excel + Online Access - 1 Year
Introduction to the Robotic Nurse Assistant opportunity, outlining core capabilities, clinical priorities, and strategic imperatives for healthcare leaders
Robotic nurse assistants are emerging as a pivotal technology at the intersection of clinical workflow transformation and healthcare operations optimization. This introduction contextualizes the technology’s core capabilities-autonomous material handling, medication management, patient monitoring, rehabilitation assistance, and telepresence-and explains how they converge to address pressing clinical priorities such as staff workload reduction, infection control, and continuity of care. The intent here is to orient executive readers to the device classes, typical deployment environments, and primary clinician and patient benefits without presupposing prior technical familiarity.
Clinicians and administrators increasingly demand solutions that integrate with electronic health records, adhere to strict safety protocols, and provide demonstrable improvements in task efficiency. Robotic nurse assistants are designed to operate within these constraints, offering repeatable, auditable task execution while freeing clinicians to focus on higher-value cognitive tasks. The remainder of this summary distills the essential strategic considerations for health system leaders, procurement teams, and technology partners contemplating pilot programs or scaled rollouts, emphasizing interoperability, clinical validation, and the organizational change aspects necessary to realize the technology’s value.
Transformative technological, operational, and clinical shifts reshaping nursing workflows and patient care through robotic nurse assistants across care settings
Over the past several years, multiple converging trends have accelerated the readiness and appetite for robotic nurse assistants. Advances in sensor fidelity and perception algorithms have improved safe interaction with patients and dynamic clinical environments, while modular hardware and software architectures have increased the speed of integration with hospital systems. At the same time, clinical pressure points-workforce shortages, rising acuity, and the need for enhanced infection prevention-have shifted executive priorities toward automation that can reliably handle routine tasks.
Operational models are also evolving: procurement teams are favoring reimbursable-capable technologies and service-oriented commercial arrangements that reduce up-front capital risk. From an implementation standpoint, successful pilots have leaned on multidisciplinary governance, measurable clinical outcomes, and iterative training programs. As these trends mature, the landscape is moving from isolated demonstrations to integrated workflows, and health systems that align procurement, clinical champions, and IT strategies will be best positioned to scale robotic nurse assistant deployments effectively.
Assessment of United States tariff changes through 2025 and their operational, procurement, and supply chain impacts on robotic nurse assistants
United States tariff measures enacted or announced through 2025 have introduced new layers of procurement complexity for organizations acquiring robotics components and finished systems. Tariff adjustments on electronics, precision components, and certain robotics assemblies can increase landed costs and elongate procurement timelines, particularly where supply chains rely on cross-border subassembly and just-in-time inventory practices. Procurement teams must therefore reconcile short-term price and timing impacts with longer-term sourcing strategies to preserve both budgetary discipline and implementation schedules.
The practical implications extend beyond cost. Tariff-driven supplier reconfiguration can alter lead times for critical parts such as actuators and vision sensors, prompting engineering teams to qualify alternative components or redesign subsystems to maintain functionality within a different supplier ecosystem. In parallel, legal and compliance teams should reassess contract terms, warranties, and maintenance agreements to reflect any changes in component origin or manufacturer responsibility. To maintain continuity of service and project momentum, decision-makers should incorporate tariff sensitivity into supplier evaluations, prioritize contractual flexibility, and develop mitigation playbooks that include dual-sourcing, inventory buffering for critical spares, and phased deployments aligned with procurement windows.
Segmentation insights on how applications, components, end-user settings, and mobility choices shape clinical value, procurement, and integration
Segmentation-driven insights illuminate how design choices and clinical applications translate into differentiated value propositions for purchasers and clinicians. When considered by application, logistics functions such as supply transport and waste disposal favor robust mobility platforms with secure payload handling and clear route mapping, whereas medication management use cases focused on adherence monitoring and automated dispensing place a premium on secure access controls, audit trails, and tight integration with pharmacy systems. Patient monitoring applications that emphasize fall detection and vital signs monitoring require high-resolution sensors and reliable event escalation pathways, while rehabilitation offerings for cognitive therapy assistance and physical therapy assistance demand adaptable interfaces and evidence-based therapeutic content. Telepresence implementations span remote consultation and remote surgery assistance, each with divergent latency, imaging, and regulatory requirements that directly influence platform selection.
Breaking the technology down by component clarifies procurement and maintenance priorities. Hardware investments concentrate on actuators and sensors; actuator selections between electric and hydraulic embodiments shape power profiles and maintenance cycles, while sensor choices such as touch and vision sensors govern interaction fidelity and environment awareness. Service components-installation, maintenance, and training-define the total cost of ownership and determine how quickly clinical staff achieve safe operational competence. Software layers, including navigation modules and user interface frameworks, further differentiate solutions; navigation software dictates autonomy levels and route optimization, while interface choices between touchscreen and voice recognition materially affect clinician acceptance and patient interaction. End-user segmentation across clinics, home healthcare, hospitals, and long-term care facilities maps directly to requirements for scale, regulatory compliance, and on-site technical support, and mobility classifications of stationary, tracked, and wheeled platforms influence physical integration, facility access, and task suitability. Taken together, these segmentation lenses offer a structured way to align clinical needs, procurement constraints, and deployment pathways to achieve reliable clinical outcomes.
Regional analysis highlighting distinct adoption drivers, regulatory dynamics, reimbursement environments, and partnership models across major global regions
Regional dynamics present materially different adoption drivers and operational constraints that affect how health systems approach robotic nurse assistant procurement and deployment. In the Americas, buyers frequently emphasize integration with diverse electronic health record systems, cost containment, and pilot-to-scale pathways that demonstrate staff time savings and measurable safety improvements. Regulatory clarity and reimbursement discussions often focus on device classification and evidence of clinical benefit, which influences timeline expectations and the structure of commercial agreements.
In Europe, Middle East & Africa, regulatory frameworks and procurement models vary significantly between jurisdictions, creating both challenges and opportunities for localization of software, language support, and service delivery. Reimbursement and public procurement mechanisms in certain European markets can favor solution providers with established clinical partnerships, whereas markets in the Middle East and Africa may emphasize rapid capability deployment and resilience in less predictable operating environments. In Asia-Pacific, accelerated adoption is driven by ageing populations, concentrated urban healthcare systems, and aggressive national digital health initiatives; these factors encourage solutions that can scale across hospital networks and accommodate language and workflow customizations. Each region’s labor dynamics, regulatory posture, and infrastructure maturity should inform vendor selection, pilot design, and expected time-to-value for deployments.
Competitive company intelligence revealing product positioning, go-to-market plays, R&D focus areas, and alliance strategies among leading providers
Company-level dynamics are central to how robotic nurse assistant solutions evolve and how customers should evaluate options. Leading providers differentiate across product modularity, integration toolkits, and service delivery models; those that offer clear upgrade paths, API-based interoperability, and structured service contracts will typically lower barriers for enterprise adoption. Competitive positioning also reflects R&D focus areas such as perception resiliency, human-robot interaction design, and autonomous navigation in cluttered clinical environments.
Strategic alliances and channel partnerships play an outsized role in accelerating deployment, particularly when robotics vendors collaborate with established medical device manufacturers, IT integrators, or global clinical services firms. From a procurement perspective, organizations should assess not only the baseline performance of platforms but also a vendor’s capacity for sustained field support, regulatory compliance track record, and roadmap alignment with hospital digitization priorities. Due diligence that incorporates site visits, proof-of-concept results, and service level commitments will reveal which providers are prepared to move from pilot to enterprise-scale support.
Actionable recommendations to prioritize clinical integration, procurement efficiency, regulatory readiness, and scalable operational deployment
Successful adoption of robotic nurse assistants requires a clear sequence of strategic and operational actions that balance clinical safeguards with scalability. First, establish multidisciplinary governance led by clinical champions, procurement, and IT to set success criteria and measurable outcomes. This governance structure should define safety thresholds, integration priorities, and phased adoption milestones so that pilots generate rigorous evidence that justifies subsequent investment. Next, prioritize interoperability by insisting on open standards, API capabilities, and integration proofs with core systems such as electronic health records and pharmacy dispensing platforms to minimize custom engineering and reduce time to clinical utility.
Operational readiness also requires targeted workforce planning and competency development. Invest in role-based training programs that accelerate confidence among nursing staff and define escalation protocols for exceptions. From a procurement standpoint, favor commercial models that align payment to outcomes where feasible, include defined service level agreements for uptime and response times, and allow for component-level upgrades without full system replacement. Finally, adopt an iterative deployment approach: begin with high-frequency, low-complexity tasks to demonstrate reliability, then progressively expand into higher-complexity clinical interactions as the technology and staff proficiency mature. This pathway minimizes disruption while producing measurable value and stakeholder buy-in.
Research methodology explaining data sources, stakeholder interviews, technology assessments, validation steps, and analytical frameworks used to inform findings
The research approach combined structured primary engagements with technology assessments and secondary analysis to triangulate insights. Primary inputs included interviews with clinicians, procurement leaders, biomedical engineers, and system integrators to capture operational realities and adoption pain points. These stakeholder conversations were supplemented by observational assessments of live pilot deployments to validate user interaction models, fault modes, and service requirements in real-world conditions.
Secondary analysis incorporated product specifications, regulatory filings, and peer-reviewed clinical literature to assess safety profiles and efficacy evidence across application areas. Technology assessments focused on hardware durability, sensor performance, actuator reliability, software architecture, and cybersecurity posture. Throughout the process, findings were validated through cross-referencing with practitioner feedback and iterative review with domain experts to ensure that conclusions reflect both empirical observation and practical applicability for decision-makers.
Strategic conclusion synthesizing implications for clinical leaders, procurement teams, technology partners, and policymakers to guide adoption decisions
In conclusion, robotic nurse assistants represent a practical lever to improve operational efficiency, enhance patient safety, and mitigate workforce constraints when implemented with disciplined governance and clear clinical objectives. The technology is most effective when narrowly scoped to high-frequency tasks that relieve clinicians of repetitive work, integrated tightly with digital health systems, and supported by vendor service models that address maintenance and training.
Decision-makers should treat early deployments as learning systems that produce evidence for broader adoption. By balancing procurement flexibility, regulatory alignment, and workforce readiness, health systems can convert pilot successes into sustainable services that enhance both clinician satisfaction and patient outcomes. The strategic imperative is to adopt a measured, data-driven pathway forward that privileges interoperable design, validated safety, and incremental scale rather than seeking immediate, wholesale replacement of existing workflows.
Please Note: PDF & Excel + Online Access - 1 Year
Table of Contents
191 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Integration of AI-driven predictive analytics in robotic nurse assistants for proactive patient care
- 5.2. Adoption of telehealth-enabled robotic nursing platforms to expand remote patient monitoring capabilities
- 5.3. Advancements in robotic dexterity and fine motor control for automated medication dispensing accuracy enhancement
- 5.4. Development of regulatory compliance frameworks and data privacy standards for healthcare robotics integration
- 5.5. Collaborative partnerships between healthcare providers and robotics startups driving customized assistant solutions
- 5.6. Emphasis on human-robot interaction design improvements to enhance patient comfort and trust in clinical settings
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Robotic Nurse Assistant Market, by Component
- 8.1. Hardware
- 8.1.1. Actuators
- 8.1.1.1. Electric Actuators
- 8.1.1.2. Hydraulic Actuators
- 8.1.2. Sensors
- 8.1.2.1. Touch Sensors
- 8.1.2.2. Vision Sensors
- 8.2. Services
- 8.2.1. Installation
- 8.2.2. Maintenance
- 8.2.3. Training
- 8.3. Software
- 8.3.1. Navigation Software
- 8.3.2. User Interface Software
- 8.3.2.1. Touchscreen Interface
- 8.3.2.2. Voice Recognition Interface
- 9. Robotic Nurse Assistant Market, by Mobility
- 9.1. Stationary
- 9.2. Tracked
- 9.3. Wheeled
- 10. Robotic Nurse Assistant Market, by Application
- 10.1. Logistics
- 10.1.1. Supply Transport
- 10.1.2. Waste Disposal
- 10.2. Medication Management
- 10.2.1. Adherence Monitoring
- 10.2.2. Dispensing
- 10.3. Patient Monitoring
- 10.3.1. Fall Detection
- 10.3.2. Vital Signs Monitoring
- 10.4. Rehabilitation
- 10.4.1. Cognitive Therapy Assistance
- 10.4.2. Physical Therapy Assistance
- 10.5. Telepresence
- 10.5.1. Remote Consultation
- 10.5.2. Remote Surgery Assistance
- 11. Robotic Nurse Assistant Market, by End User
- 11.1. Clinics
- 11.2. Home Healthcare
- 11.3. Hospitals
- 11.4. Long-Term Care Facilities
- 12. Robotic Nurse Assistant Market, by Region
- 12.1. Americas
- 12.1.1. North America
- 12.1.2. Latin America
- 12.2. Europe, Middle East & Africa
- 12.2.1. Europe
- 12.2.2. Middle East
- 12.2.3. Africa
- 12.3. Asia-Pacific
- 13. Robotic Nurse Assistant Market, by Group
- 13.1. ASEAN
- 13.2. GCC
- 13.3. European Union
- 13.4. BRICS
- 13.5. G7
- 13.6. NATO
- 14. Robotic Nurse Assistant Market, by Country
- 14.1. United States
- 14.2. Canada
- 14.3. Mexico
- 14.4. Brazil
- 14.5. United Kingdom
- 14.6. Germany
- 14.7. France
- 14.8. Russia
- 14.9. Italy
- 14.10. Spain
- 14.11. China
- 14.12. India
- 14.13. Japan
- 14.14. Australia
- 14.15. South Korea
- 15. Competitive Landscape
- 15.1. Market Share Analysis, 2024
- 15.2. FPNV Positioning Matrix, 2024
- 15.3. Competitive Analysis
- 15.3.1. ABB Ltd.
- 15.3.2. Aethon, Inc.
- 15.3.3. Cyberdyne, Inc.
- 15.3.4. Cyberdyne, Inc.
- 15.3.5. Hstar Technologies, Inc.
- 15.3.6. Intuitive Surgical, Inc.
- 15.3.7. KUKA AG
- 15.3.8. Omron Corporation
- 15.3.9. Panasonic Healthcare Co., Ltd.
- 15.3.10. PARO Robots U.S., Inc.
- 15.3.11. Relay Robotics, Inc.
- 15.3.12. Richtech Robotics Inc.
- 15.3.13. Siasun Robot & Automation Co., Ltd.
- 15.3.14. SoftBank Robotics Group Corp.
- 15.3.15. Stryker Corporation
- 15.3.16. Toyota Motor Corporation
- 15.3.17. Vecna Robotics, Inc.
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